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Author: Artur S. d'Avila Garcez Publisher: Springer Science & Business Media ISBN: 1447102118 Category : Computers Languages : en Pages : 276
Book Description
Artificial Intelligence is concerned with producing devices that help or replace human beings in their daily activities. Neural-symbolic learning systems play a central role in this task by combining, and trying to benefit from, the advantages of both the neural and symbolic paradigms of artificial intelligence. This book provides a comprehensive introduction to the field of neural-symbolic learning systems, and an invaluable overview of the latest research issues in this area. It is divided into three sections, covering the main topics of neural-symbolic integration - theoretical advances in knowledge representation and learning, knowledge extraction from trained neural networks, and inconsistency handling in neural-symbolic systems. Each section provides a balance of theory and practice, giving the results of applications using real-world problems in areas such as DNA sequence analysis, power systems fault diagnosis, and software requirements specifications. Neural-Symbolic Learning Systems will be invaluable reading for researchers and graduate students in Engineering, Computing Science, Artificial Intelligence, Machine Learning and Neurocomputing. It will also be of interest to Intelligent Systems practitioners and anyone interested in applications of hybrid artificial intelligence systems.
Author: Artur S. d'Avila Garcez Publisher: Springer Science & Business Media ISBN: 1447102118 Category : Computers Languages : en Pages : 276
Book Description
Artificial Intelligence is concerned with producing devices that help or replace human beings in their daily activities. Neural-symbolic learning systems play a central role in this task by combining, and trying to benefit from, the advantages of both the neural and symbolic paradigms of artificial intelligence. This book provides a comprehensive introduction to the field of neural-symbolic learning systems, and an invaluable overview of the latest research issues in this area. It is divided into three sections, covering the main topics of neural-symbolic integration - theoretical advances in knowledge representation and learning, knowledge extraction from trained neural networks, and inconsistency handling in neural-symbolic systems. Each section provides a balance of theory and practice, giving the results of applications using real-world problems in areas such as DNA sequence analysis, power systems fault diagnosis, and software requirements specifications. Neural-Symbolic Learning Systems will be invaluable reading for researchers and graduate students in Engineering, Computing Science, Artificial Intelligence, Machine Learning and Neurocomputing. It will also be of interest to Intelligent Systems practitioners and anyone interested in applications of hybrid artificial intelligence systems.
Author: Artur S. D'Avila Garcez Publisher: Springer Science & Business Media ISBN: 3540732454 Category : Computers Languages : en Pages : 200
Book Description
This book explores why, regarding practical reasoning, humans are sometimes still faster than artificial intelligence systems. It is the first to offer a self-contained presentation of neural network models for many computer science logics.
Author: P. Hitzler Publisher: IOS Press ISBN: 1643682458 Category : Computers Languages : en Pages : 410
Book Description
Neuro-symbolic AI is an emerging subfield of Artificial Intelligence that brings together two hitherto distinct approaches. ”Neuro” refers to the artificial neural networks prominent in machine learning, ”symbolic” refers to algorithmic processing on the level of meaningful symbols, prominent in knowledge representation. In the past, these two fields of AI have been largely separate, with very little crossover, but the so-called “third wave” of AI is now bringing them together. This book, Neuro-Symbolic Artificial Intelligence: The State of the Art, provides an overview of this development in AI. The two approaches differ significantly in terms of their strengths and weaknesses and, from a cognitive-science perspective, there is a question as to how a neural system can perform symbol manipulation, and how the representational differences between these two approaches can be bridged. The book presents 17 overview papers, all by authors who have made significant contributions in the past few years and starting with a historic overview first seen in 2016. With just seven months elapsed from invitation to authors to final copy, the book is as up-to-date as a published overview of this subject can be. Based on the editors’ own desire to understand the current state of the art, this book reflects the breadth and depth of the latest developments in neuro-symbolic AI, and will be of interest to students, researchers, and all those working in the field of Artificial Intelligence.
Author: Alexiei Dingli Publisher: Packt Publishing Ltd ISBN: 1804616958 Category : Computers Languages : en Pages : 196
Book Description
Explore the inner workings of AI along with its limitations and future developments and create your first transparent and trustworthy neuro-symbolic AI system Purchase of the print or Kindle book includes a free PDF eBook Key Features Understand symbolic and statistical techniques through examples and detailed explanations Explore the potential of neuro-symbolic AI for future developments using case studies Discover the benefits of combining symbolic AI with modern neural networks to build transparent and high-performance AI solutions Book Description Neuro-symbolic AI offers the potential to create intelligent systems that possess both the reasoning capabilities of symbolic AI along with the learning capabilities of neural networks. This book provides an overview of AI and its inner mechanics, covering both symbolic and neural network approaches. You'll begin by exploring the decline of symbolic AI and the recent neural network revolution, as well as their limitations. The book then delves into the importance of building trustworthy and transparent AI solutions using explainable AI techniques. As you advance, you'll explore the emerging field of neuro-symbolic AI, which combines symbolic AI and modern neural networks to improve performance and transparency. You'll also learn how to get started with neuro-symbolic AI using Python with the help of practical examples. In addition, the book covers the most promising technologies in the field, providing insights into the future of AI. Upon completing this book, you will acquire a profound comprehension of neuro-symbolic AI and its practical implications. Additionally, you will cultivate the essential abilities to conceptualize, design, and execute neuro-symbolic AI solutions. What you will learn Gain an understanding of the intuition behind neuro-symbolic AI Determine the correct uses that can benefit from neuro-symbolic AI Differentiate between types of explainable AI techniques Think about, design, and implement neuro-symbolic AI solutions Create and fine-tune your first neuro-symbolic AI system Explore the advantages of fusing symbolic AI with modern neural networks in neuro-symbolic AI systems Who this book is for This book is ideal for data scientists, machine learning engineers, and AI enthusiasts who want to explore the emerging field of neuro-symbolic AI and discover how to build transparent and trustworthy AI solutions. A basic understanding of AI concepts and familiarity with Python programming are needed to make the most of this book.
Author: Navdeep Kaur Publisher: ISBN: Category : Artificial intelligence Languages : en Pages :
Book Description
Much has been achieved in AI but to realize its true potential, it is imperative that the AI system should be able to learn generalizable and actionable higher-level knowledge from lowest level percepts. Inspired by this goal, neuro-symbolic systems have been developed for the past four decades. These systems encompass the complementary strengths of fast adaptive learning of neural networks from low-level input signals and the deliberative, generalizable models of the symbolic systems. The advent of deep networks has accelerated the development of these neuro-symbolic systems. While successful, there are several open problems to be addressed in these systems, a few of which we tackle in this dissertation. These include: (i) several primitive neural network architectures have not been well studied in the symbolic context; (ii) lack of generic neuro-symbolic architectures that are do not make distributional assumptions; (iii) generalization abilities of many such systems are limited. The objective of this dissertation is to develop novel neuro-symbolic models that (i) induce symbolic reasoning capabilities to fundamental yet unexplored neural network architectures, and (ii) provide unique solutions to the generalization issues that occur during neuro-symbolic integration. More specifically, we consider one of the primitive models, Restricted Boltzmann Machines, that was originally employed for pre-training the deep neural networks and propose two unique solutions to lift them for relational model. For the first solution, we employ relational random walks to generate relational features for Boltzmann machines. We train the Boltzmann machines by passing these resulting features through a novel transformation layer. For the second solution, we employ the mechanism of functional gradient boosting to learn the structure and the parameters of the lifted Restricted Boltzmann Machines simultaneously. Next, most of the neuro-symbolic models designed till date have focused on incorporating neural capabilities in specific models, resulting in lack of a general relational neural network architecture. To overcome this, we develop a generic neuro-symbolic architecture that exploits the concept of relational parameter tying and combining rules to incorporate the first-order logic rules into the hidden layers of the proposed architecture. One of the prevalent neuro-symbolic models called knowledge graph embedding models encode the symbols as learnable vectors in Euclidean space and lose an important characteristic of generalizability to newer symbols while doing so. We propose two unique solutions to circumvent this problem by exploiting the text description of entities in addition to the knowledge graph triples in both the models. In our first model, we train both the text and knowledge graph data in generative setting, while in the second model, we posit the two data sources in adversarial setting. Our broad results across these several directions demonstrate the efficacy and efficiency of the proposed approaches on benchmarks and novel data sets. In summary, this dissertation takes one of the first steps towards realizing the grand vision of the neuro-symbolic integration by proposing novel models that allow for symbolic reasoning capabilities inside neural networks.
Author: Dawn E. Holmes Publisher: Springer Science & Business Media ISBN: 3540306099 Category : Computers Languages : en Pages : 285
Book Description
Machine learning is currently one of the most rapidly growing areas of research in computer science. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. This book covers the three main learning systems; symbolic learning, neural networks and genetic algorithms as well as providing a tutorial on learning casual influences. Each of the nine chapters is self-contained. Both theoreticians and application scientists/engineers in the broad area of artificial intelligence will find this volume valuable. It also provides a useful sourcebook for Postgraduate since it shows the direction of current research.
Author: Stephen José Hanson Publisher: Mit Press ISBN: 9780262581332 Category : Computers Languages : en Pages : 449
Book Description
Annotation These original contributions converge on an exciting and fruitful intersection of three historically distinct areas of learning research: computational learning theory, neural networks, and symbolic machine learning. Bridging theory and practice, computer science and psychology, they consider general issues in learning systems that could provide constraints for theory and at the same time interpret theoretical results in the context of experiments with actual learning systems. In all, nineteen chapters address questions such as, What is a natural system? How should learning systems gain from prior knowledge? If prior knowledge is important, how can we quantify how important? What makes a learning problem hard? How are neural networks and symbolic machine learning approaches similar? Is there a fundamental difference in the kind of task a neural network can easily solve as opposed to those a symbolic algorithm can easily solve? Stephen J. Hanson heads the Learning Systems Department at Siemens Corporate Research and is a Visiting Member of the Research Staff and Research Collaborator at the Cognitive Science Laboratory at Princeton University. George A. Drastal is Senior Research Scientist at Siemens Corporate Research. Ronald J. Rivest is Professor of Computer Science and Associate Director of the Laboratory for Computer Science at the Massachusetts Institute of Technology.
Author: Tiansi Dong Publisher: Springer Nature ISBN: 3030562751 Category : Technology & Engineering Languages : en Pages : 155
Book Description
The unification of symbolist and connectionist models is a major trend in AI. The key is to keep the symbolic semantics unchanged. Unfortunately, present embedding approaches cannot. The approach in this book makes the unification possible. It is indeed a new and promising approach in AI. -Bo Zhang, Director of AI Institute, Tsinghua It is indeed wonderful to see the reviving of the important theme Nural Symbolic Model. Given the popularity and prevalence of deep learning, symbolic processing is often neglected or downplayed. This book confronts this old issue head on, with a historical look, incorporating recent advances and new perspectives, thus leading to promising new methods and approaches. -Ron Sun (RPI), on Governing Board of Cognitive Science Society Both for language and humor, approaches like those described in this book are the way to snickerdoodle wombats. -Christian F. Hempelmann (Texas A&M-Commerce) on Executive Board of International Society for Humor Studies
Author: Tarek R. Besold Publisher: Springer ISBN: 9783031711664 Category : Computers Languages : en Pages : 0
Book Description
This book constitutes the refereed proceedings of the 18th International Conference on Neural-Symbolic Learning and Reasoning, NeSy 2024, held in Barcelona, Spain during September 9-12th, 2024. The 30 full papers and 18 short papers were carefully reviewed and selected from 89 submissions, which presented the latest and ongoing research work on neurosymbolic AI. Neurosymbolic AI aims to build rich computational models and systems by combining neural and symbolic learning and reasoning paradigms. This combination hopes to form synergies among their strengths while overcoming their complementary weaknesses.
Author: Stefan Wermter Publisher: Springer Science & Business Media ISBN: 3540673059 Category : Computers Languages : en Pages : 411
Book Description
Hybrid neural systems are computational systems which are based mainly on artificial neural networks and allow for symbolic interpretation or interaction with symbolic components. This book is derived from a workshop held during the NIPS'98 in Denver, Colorado, USA, and competently reflects the state of the art of research and development in hybrid neural systems. The 26 revised full papers presented together with an introductory overview by the volume editors have been through a twofold process of careful reviewing and revision. The papers are organized in the following topical sections: structured connectionism and rule representation; distributed neural architectures and language processing; transformation and explanation; robotics, vision, and cognitive approaches.
Author: Artur S. d'Avila Garcez
Author: Artur S. d'Avila Garcez
Author: Artur S. D'Avila Garcez
Author: P. Hitzler
Author: Alexiei Dingli
Author: Navdeep Kaur
Author: Dawn E. Holmes
Author: Stephen José Hanson
Author: Tiansi Dong
Author: Tarek R. Besold
Author: Stefan Wermter